Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 911–920 of 1718 papers

Title	Date	Tasks	Status
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning	Apr 4, 2023	Action AnticipationMulti-agent Reinforcement Learning	—Unverified
Risk-Aware Distributed Multi-Agent Reinforcement Learning	Apr 4, 2023	Decision MakingMulti-agent Reinforcement Learning	—Unverified
Regularization of the policy updates for stabilizing Mean Field Games	Apr 4, 2023	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering	Mar 31, 2023	Graph Neural NetworkMulti-agent Reinforcement Learning	—Unverified
Selective Reincarnation: Offline-to-Online Multi-Agent Reinforcement Learning	Mar 31, 2023	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies	Mar 29, 2023	global-optimizationMulti-agent Reinforcement Learning	—Unverified
Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile Communications	Mar 29, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
The challenge of redundancy on multi-agent value factorisation	Mar 28, 2023	Multi-agent Reinforcement Learning	—Unverified
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football	Mar 25, 2023	Decision MakingImitation Learning	—Unverified
Learning Reward Machines in Cooperative Multi-Agent Tasks	Mar 24, 2023	Multi-agent Reinforcement Learning	—Unverified

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All datasets ParticleEnvs Cooperative Communication SMAC-Exp UAV Logistics

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MATD3	final agent reward	-14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRIMA	Median Win Rate	15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Fusion-Multi-Actor-Attention-Critic	Average Reward	39	—	Unverified